In most digital cameras, there exists a nonlinear relationship between the image intensities output from the camera and the scene radiance that enters the camera. This non-linearity is intentionally designed into digital cameras to compress the dynamic range of scenes and to account for nonlinearities in display systems. However, in some applications, it is desired to calibrate the digital camera (or other image capture devices) so that the intensities of the image will more accurately correspond to the scene radiance received by the camera.
FIG. 1 shows an example of a conventional system 100 for capturing an image of a scene 102 to produce a digital color image 104. The system 100 includes a digital camera 106 to capture the image of the scene 102 and output measured colors that are used to form the digital image 104. The digital camera 106 has a response function 108 that relates the colors of digital image 104 to the scene radiance from scene 102. This mapping of scene radiance to image intensity of colors is also referred to as the “radiometric response function”. More particularly, radiance from a scene is recorded at the imaging array of a camera (e.g., a CCD array) as image irradiance. Irradiance values I are then transformed according to the camera's radiometric response f into measured intensities M that are output from the camera:M=f(I)
In general, the radiometric response function 108 is nonlinear and depends on the camera, typically varying from camera to camera. Further, the response function 108 can even be different for cameras that are the same model.
While the intentional nonlinear response function may be beneficial for viewing purposes, it impairs many computer vision methods which assume that image intensities are linearly related to scene radiance. A broad range of vision algorithms require linearity because precise measurements of scene radiance are needed for accurate processing. In photometric methods, such as shape from shading, color constancy, and illumination estimation, physical information is derived from scene radiance for analyzing a scene. Image intensities are also implicitly presumed to convey scene radiance in many other vision methods, such as object recognition and multi-camera stereo, so that images captured from different cameras or at different brightness levels can be properly compared.
To compensate for the nonlinear response function 108, camera 102 can be calibrated by finding an “inverse” of response function 108 so that, ideally, the measured colors will be mapped into colors exactly matching or linearly related to the scene radiance. There are conventional approaches to finding this inverse response function. In one approach, a user takes an image of a “reference” color scene (i.e., having regions of known color) so that the measured colors output by camera 102 can be compared to the actual colors. Thus, this type of approach requires an image of the “reference”. In another approach, several images of a scene are required. The series of images are taken under various precisely known exposure settings, with all of the images being registered (i.e., taken with the positions of the camera and scene being unchanged).
However, these conventional solutions have shortcomings in that in some scenarios, neither a “reference” image captured by the camera nor a series of registered images with different exposure settings may be available. For example, in some scenarios, only a single image may be available, with no knowledge of the camera and the exposure setting used to capture the image. Moreover, some cameras that capture images in grayscale (e.g., relatively inexpensive surveillance cameras) do not permit exposure changes, and thus it is impossible to take a series of images at different exposure settings.
Accordingly, there is a continuing need for improved radiometric calibration techniques.